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Record W2140603241 · doi:10.1109/robot.2005.1570459

Fuzzy Enhanced Control of an Underactuated Finger Using Tactile and Position Sensors

2005· article· en· W2140603241 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRobot Manipulation and Learning
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsUnderactuationTactile sensorComputer scienceScheme (mathematics)Controller (irrigation)Control theory (sociology)Position (finance)Fuzzy logicFuzzy control systemControl engineeringControl (management)SlippageArtificial intelligenceEngineeringRobotMathematics

Abstract

fetched live from OpenAlex

This paper proposes a control scheme dedicated to underactuated fingers with the intention of maximizing the capabilities of the latter using tactile and position information at a minimum cost. Tactile sensors are implemented on one prototype of underactuated finger and used to enhance the behaviour of the hand despite its limited number of control signals. First, tactile technology is briefly recalled and discussed. Second, the electronic design of the sensors' controller is presented. Third, a real-time control scheme is introduced, based on a fuzzy force control method. Finally, a slippage prevention technique is presented. Results are discussed based on experimental observations and indicate that the behaviour of underactuated fingers can be substantially enhanced with tactile information and a classic fuzzy control approach.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.241
Teacher spread0.228 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Quick stats

Citations62
Published2005
Admission routes1
Has abstractyes

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